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The National Ignition Facility (NIF) is now producing experimental results for the study of inertial confinement fusion (ICF). These results are captured by complex diagnostic systems and are key to achieving NIF's goal to demonstrate thermonuclear burn of deuterium and tritium fuel in a laboratory setting. High bandwidth gamma-ray fusion-burn measurements and soft X-ray indirect and direct drive energetic measurements are both captured with oscilloscope recording systems that distort or modulate the raw data. The Shot Data Analysis team has developed signal processing corrections for these oscilloscope recording systems through an automated engine. Once these corrections are applied, accurate fundamental quantities can be discerned.

The National Ignition Facility (NIF) at the Lawrence Livermore National Laboratory is the world's most energetic laser, providing a scientific research center to study inertial confinement fusion and matter at extreme energy densities and pressures. A target shot involves over 30 specialized diagnostics measuring critical x-ray, optical and nuclear phenomena to quantify ignition results for comparison with computational models. The Shot Analysis and Visualization System (SAVI) acquires and analyzes target diagnostic data for display within a time-budget of 30 min. Laser and target diagnostic data are automatically loaded into the NIF archive database through clustered software data collection agents. The SAVI Analysis Engine distributes signal and image processing tasks to a Linux cluster where computation is performed. Intermediate results are archived at each step of the analysis pipeline. Data is archived with metadata and pedigree. Experiment results are visualized through a web-based user interface in interactive dashboards tailored to single or multiple shot perspectives. The SAVI system integrates open-source software, commercial workflow tools, relational database and messaging technologies into a service-oriented and distributed software architecture that is highly parallel, scalable, and flexible. The architecture and functionality of the SAVI system will be presented along with examples.

Technology has been integrated into academia. The 21st century learner will be a life-long learner and students need to be comfortable and familiar with computers and technology. Computers are part of the educational environment independent of the different learning styles of the students. Regardless of the major field of study elected by a student, the use of library resources is necessary. Library technophobia is a fear of using a library because of the technology involved. This article addresses different groups who may suffer from library technophobia, the reasons they are anxious, and positive steps that can be taken to ameliorate this stumbling block to academic success.

The National Ignition Facility (NIF) is producing experimental results for the study of Inertial Confinement Fusion (ICF). The Gamma Reaction History (GRH) diagnostic at NIF can detect gamma rays to measure fusion burn parameters such as fusion burn width, bang time, neutron yield, and areal density of the compressed ablator for cryogenic deuterium-tritium (DT) implosions. Gamma-ray signals detected with this diagnostic are inherently distorted by hardware impulse response functions (IRFs) and gains, and are comprised of several components including gamma rays from laser-plasma interactions (LPI). One method for removing hardware distortions to approximate the gamma-ray reaction history is deconvolution. However, deconvolution of the distorted signal to obtain the gamma-ray reaction history and its associated parameters presents an ill-posed inverse problem and does not separate out the source components of the gamma-ray signal. A multi-dimensional parameter space model for the distorted gamma-ray signal has been developed in the literature. To complement a deconvolution, we develop a multi-objective optimization algorithm to determine the model parameters so that the error between the model and the collected gamma-ray data is minimized in the least-squares sense. The implementation of the optimization algorithm must be suffciently robust to be used in automated production software. To achieve this level of robustness, impulse response signals must be carefully processed and constraints on the parameter space based on theory and experimentation must be implemented to ensure proper convergence of the algorithm. In this paper, we focus on the optimization algorithm's theory and implementation.

Cognitive research shows that student motivation, perception, comprehension, and recall is improved by using a variety of instructional strategies. After a discussion of how individuals learn through cognitive processing, this paper suggests the use of a variety of strategies for motivating students and enhancing learning. As a particular example, cooperative learning groups are describeb in detail. In these groups students assume responsibility not only for their own learning but also the learning of others.

Some diagnostics at the National Ignition Facility (NIF), including the Gamma Reaction History (GRH) diagnostic, require multiple channels of data to achieve the required dynamic range. These channels need to be stitched together into a single time series, and they may have non-uniform and redundant time samples. We chose to apply the popular cubic smoothing spline technique to our stitching problem because we needed a general non-parametric method. We adapted one of the algorithms in the literature, by Hutchinson and deHoog, to our needs. The modified algorithm and the resulting code perform a cubic smoothing spline fit to multiple data channels with redundant time samples and missing data points. The data channels can have different, time-varying, zero-mean white noise characteristics. The method we employ automatically determines an optimal smoothing level by minimizing the Generalized Cross Validation (GCV) score. In order to automatically validate the smoothing level selection, the Weighted Sum-Squared Residual (WSSR) and zero-mean tests are performed on the residuals. Further, confidence intervals, both analytical and Monte Carlo, are also calculated. In this paper, we describe the derivation of our cubic smoothing spline algorithm. We outline the algorithm and test it with simulated and experimental data.

This paper presents Dynamic Chiller Sequencing (DCS); an algorithm for controlling the HVAC equipment necessary to cool non-residential buildings which balances the trade-off between reducing the cost of electricity used and the amount charged for peak power consumption. This is accomplished by forecasting the cooling loads expected through a planning horizon, determining the minimum cost way of meeting the individual loads with various combinations of equipment, and using a modified shortest path algorithm to determine the sequence of equipment selection that will minimize the cost of satisfying the expected loads for the entire planning horizon. Although the strategy was developed for the control of HVAC equipment, it can be generalized for solving sequencing decisions in process industries with competing, sequence dependent costs.

Air in most non-residential buildings needs to be cooled to maintain occupant comfort. Typically, the air is cooled by using cold water provided by a chiller plant consisting of liquid chillers, cooling towers and pumps, This paper presents a mathematical programming approach for determining which available chiller plant equipment to use to meet a cooling load as well as the best operating temperatures for the water flows throughout the system. First, a mixed-integer, nonlinear formulation of the problem is developed. A heuristic approach for handling the integer variables is then presented which allows very good solutions to be obtained by solving a series of continuous problems using sequential quadratic programming (SQP). Finally, regression analysis of a large number of optimizations arc presented which show a linear trend that can be used to help guide the chilled water control system to make long term control decisions.

Many automated image-based applications have need of finding small spots in a variably noisy image. For humans, it is relatively easy to distinguish objects from local surroundings no matter what else may be in the image. We attempt to capture this distinguishing capability computationally by calculating a measurement that estimates the strength of signal within an object versus the noise in its local neighborhood. First, we hypothesize various sizes for the object and corresponding background areas. Then, we compute the Local Area Signal to Noise Ratio (LASNR) at every pixel in the image, resulting in a new image with LASNR values for each pixel. All pixels exceeding a pre-selected LASNR value become seed pixels, or initiation points, and are grown to include the full area extent of the object. Since growing the seed is a separate operation from finding the seed, each object can be any size and shape. Thus, the overall process is a 2-stage segmentation method that first finds object seeds and then grows them to find the full extent of the object. This algorithm was designed, optimized and is in daily use for the accurate and rapid inspection of optics from a large laser system (National Ignition Facility (NIF), Lawrence Livermore National Laboratory, Livermore, CA), which includes images with background noise, ghost reflections, different illumination and other sources of variation.

That National Ignition Facility (NIF) at Lawrence Livermore National Laboratory (LLNL) will be the world's largest and most energetic laser. It has thousands of optics and depends heavily on the quality and performance of these optics. Over the past several years, we have developed the NIF Optics Inspection Analysis System that automatically finds defects in a specific optic by analyzing images taken of that optic. This paper describes a new and complementary approach for the automatic detection of defects based on detecting the diffraction ring patterns in downstream optic images caused by defects in upstream optics. Our approach applies a robust pattern matching algorithm for images called Gradient Direction Matching (GDM). GDM compares the gradient directions (the direction of flow from dark to light) of pixels in a test image to those of a specified model and identifies regions in the test image whose gradient directions are most in line with those of the specified model. For finding rings, we use luminance disk models whose pixels have gradient directions all pointing toward the center of the disk. After GDM identifies potential rings locations, we rank these rings by how well they fit the theoretical diffraction ring pattern equation. We perform false alarm mitigation by throwing out rings of low fit. A byproduct of this fitting procedure is an estimate of the size of the defect and its distance from the image plane. We demonstrate the potential effectiveness of this approach by showing examples of rings detected in real images of NIF optics.

A new method for solving discrete structural optimization problems is presented. An interior penalty function is used to convert the original constrained problem into an unconstrained parametric problem. Then the search for the optimal solution to the parametric problem is based on a discrete direction gradient. Solving an appropriate sequence of these unconstrained parametric problems is equivalent to solving the original constrained optimization problem. This method is illustrated first on a small reinforced concrete problem, and then to the design of steel building frames which are made up of standard sections. Results for a one-story four-bay unsymmetrical frame and an eight-story three-bay symmetrical frame are described.

This paper presents a procedure for routing buses on an express basis between commuter stations and specific locations of high worker density. The use of two integer programming models based on a set of predetermined alternative routes is explored to develop efficient routings when characteristics such as distance, cost, and time span must be considered. When one of these models was used to develop routes for a possible additional express bus system in Chicago, the computer-based optimization resulted in routes somewhat better than an original set of routes derived manually. Although the relatively minor improvement (3.7%) achieved by the computer-based process might not always be worth the additional expense such effort always entails, the use of mathematical programming models enables extensive sensitivity analysis to be undertaken relatively painlessly and enables a wide variety of alternatives to be examined.

One hundred eighty-nine ambulatory arthritis patients were interviewed to assess needs for information about community resource services. Participants were asked how frequently they needed 66 specific information items. Three categories of resource needs in arthritis care were identified: general information, personal care and medical services, and assistive devices and equipment. In the first category, results indicated that the greater the impairment in functional ability, the greater the need for general information about arthritis. When functional ability was held constant, younger patients with lower family income displayed a greater need for arthritis resource information. Lower functional ability and family income were associated with a greater need for personal care and medical service information. Lower functional ability contributed the most to the need for information about assistive devices and equipment. We suggest that, as an essential component of the long-term care of patients with rheumatic diseases, individuals who provide educational counseling should clearly understand the community resource needs of patients, and should be able to match articulated needs with the existing services. Health professionals should be aware that certain patient characteristics (specifically, family income, functional ability, and age) are related to a need for this type of information. The large percentage of patients who could not name their rheumatic disease diagnosis (65%) presents a major concern and challenge to patient educators.

This paper proposes a model and solution procedure for the optimal design of flexible pavements that finds the best cost achievable for each possible combination of materials without actually computing the layer thicknesses. The primal-dual relationships and complementary slackness conditions of linear programming are used to reduce the required number of computations necessary to obtain the optimal design. The methodology can also be used for pavement structures with more than three layers and can be extended to cases in which there are also design specifications on the overall total thickness of the structure.

This paper presents an 0 (n log n) heuristic algorithm for the Rectilinear Steiner Minimal Tree (RSMT) problem. The algorithm is based on a decomposition approach which first partitions the vertex ...

Buildings contain enormously complex configurations of corridors, pipes, ducts, conduits and related building system components. In many cases, the configuration and analysis of these component systems can be Formulated as the Steiner tree problem with and without obstacles. Certain building systems can be represented with a Euclidean metric while others are best defined with a rectilinear (Manhattan) metric. The paper describes heuristic algorithms for the interactive development of minimal Steiner trees and circuits. The interactive approach enables architects and engineers to design these systems in ways consistent with the designed arrangement of building activities in two and three dimensions.

The United States is faced with allocating funds from a limited budget to the repair or replacement of railroad track segments on military bases. The problem can be modeled as a very large binary knapsack problem having a single budget constraint and multiple precedence constraints. A procedure has been developed to convert this original problem into an equivalent knapsack problem with a single constraint and significantly fewer variables. The new knapsack problem, still too large to be solved optimally, is then solved efficiently by a heuristic which provides answers within a few percent of optimality. Furthermore, the quality of the solution improves with the size of the problem.

A model developed to aid a state-level resource allocation process in the United States Department of Agriculture Special Supplement Food Program for Women, Infants, and Children (WIC) is presented. The model is formulated as a linear integer goal program, utilizing service levels for six categories of WIC participants as goals. The model employs a methodical, consistent approach to the allocation process, yet allows ample flexibility for consideration of state-specific issues. By allowing the user to adjust the relative importance of each goal, the model can incorporate subjective attitudes of state-level WIC administrators. This is extremely important as these attitudes are the result of the administrators' familiarity with the WIC program in their states, and the significance of input of this nature should not be neglected. An application of the model to the Indiana WIC program is presented. Sensitivity of the model to changes in the objective function weights and in the target values for the goals is explored. Although orginally developed to allocate budget increases, the model may be used in allocating budget cuts as well; this form of the model is also presented. The model can be extended hierarchically to incorporate resource allocation at the federal level. This modeling approach should be useful in all public sector programs characterized by multiple objective and hierarchical decisionmaking.

This paper discusses the use of a modified nonlinear simplex method for constrained engineering optimization problems in which some or all of the variables can only take on discrete values. The objective function and constraints need not be analytic expressions; it is only necessary that their values be computable. The modifications to the nonlinear simplex method include the incorporation of unidimensional search, new acceleration and regeneration methods, and the exploration of decomposition strategies. The algorithm has been successfully tested using problems selected to represent a variety of engineering design applications.

A method for replacing undersized bridges under limited budgets for a specified planning horizon has been developed and applied to the agricultural area of Young America, Illinois. The period of the planning horizon are considered in any arbitrary order, and a subhorizon is defined between the beginning of the overall planning horizon and each period. For each subhorizon, the most cost‐effective bridge replacement policy is selected, according to a route assignment procedure. This procedure is a heuristic method that identifies the most important users of a transportation system and sequentially reroutes them, starting with the most economical routes and finishing with routes without undersized bridges for any user.

Data from a hypertension screening project involving 4,272 black residents of a rural southern community were analyzed to determine the effects of a set of admission-decision rules on the case load of a proposed hypertension clinic. Four decision rules were investigated: conjunctive (diastolic high or systolic high); disjunctive (diastolic and/or systolic high); additive (sum of diastolic and systolic high); and systolic only. Most information relevant to admission to treatment came from knowledge of systolic blood pressures, even though knowledge of the diastolic pressure is essential in individual diagnosis. Incremental increases in the minimum blood pressure necessary for admission to treatment from 140/90 mm Hg to 160/95 mm Hg resulted in a one-third reduction in the number of patients treated, a 24% reduction in personnel utilization per patient, and a 34% reduction in drug costs; but in an estimated 14% increase in the attack rate for morbid events in men.

This paper describes the design of an O( N 2 ) heuristic algorithm for the Directed Steiner Minimal Tree (DSMT) problem in the plane. The algorithm employs the Delaunay triangulation, a minimum spanning arborescence algorithm, and a triangulation of the arborescence to develop solutions to the DSMT problem. Example applications are also included.

The paper describes a systematic approach to solving design problems with multiple conflicting objectives. MOEDM (Multiple Objective Engineering Design Methodology) is described in detail by means of an example which demonstrates how information is accumulated which leads to a logical and efficient multi-criteria design.

In this research several search methods for unconstrained nonlinear discrete variable optimization problems have been developed. Many of these new methods are modifications of effective continuous variable search techniques including gradient–free and gradient–based methods. In order to search only over a set of discrete points, the concepts of integer search direction and the subsequential search procedure are introduced. Other developments include regeneration/ acceleration procedures for gradient–based methods and a second level acceleration procedure applicable to both gradient–free and gradient–based methods. These new methods have been compared with each other and existing techniques using test problems with various characteristics, including penalty functions from constrained problems. In all cases, the best results have been obtained from one of the new methods. Moreover, the success of these new methods in finding good solutions in penalty function problems indicates their usefulness in solving e...

There are many issues which underly the effective implementation of operations research techniques on a computer. This study discusses general implementation issues, including portability, user friendliness and marketing, as well as issues specifically related to optimization. In the latter category, issues such as model input and solution output, sensitivity analysis, interactive modeling and automated data collection are examined.

An O(n log n) heuristic for the Euclidean Steiner Minimal Tree (ESMT) problem is presented. The algorithm is based on a decomposition approach which first partitions the vertex set into triangles via the Delaunay triangulation, then “recomposes” the suboptimal Steiner Minimal Tree (SMT) according to the Voronoi diagram and Minimum Spanning Tree (MST) of the point set. The ESMT algorithm was implemented in FORTRAN-IV and tested on a number of randomly generated point sets in the plane drawn from a uniform distribution. Comparison of the O(n log n) algorithm with an O(n4) algorithm clearly indicates that the O(n log n) algorithm is as good as the previous O(n4) algorithm in achieving reductions in the ratio SMT/MST of the given vertex set. This is somewhat surprising since the O(n4) algorithm considers more potential Steiner points and alternative tree configurations.

A zero-one integer-programming formulation of the simultaneous optimization of the problems of land-use assignment and transportation-network design is presented. The problem is modeled through a set-partitioning approach and incorporates a multiple-criteria objective function, appropriate upper- and lower-bound constraints on area assignments, and construction costs. A simple example and a more complicated urban-design case study are included to demonstrate the viability of this approach in solving the simultaneous-optimization problem. As a secondary benefit, this set-partitioning model can be reformulated as an integer, generalized-network, flow problem for which new efficient computer codes, capable of solving networks with thousands of nodes and variables, are available.

The participation of women as students and as faculty members in the College of Engineering at the University of Illinois in Urbana-Champaign is examined in this paper. The enrollment of undergraduate women in engineering and the awarding of engineering degrees to them is compared with nationwide averages. Recent efforts undertaken by the University of Illinois to attract more women into the engineering curriculum are discussed next, including the sponsorship of a two-day conference for high school girls to introduce them to engineering and the publication of an award winning guidance brochure. Finally, the low frequency of women on the teaching faculty in engineering departments is considered, and the potential effects of current affirmative action in the tight academic marketplace are evaluated.

the additions of new patients. The model can be used to study the impact of new patients on any ambulatory care program or any health maintenance organization in which there is unusually high personnel resource utilization during a patient's first visit. IN AMBULATORY health care organizations, the start-up effects of new patients may create unforeseen bottlenecks in the ongoing process of health care. These transient effects stem primarily from the significantly greater number of health care procedures and registration matters that must be completed at the time of the first visit of a patient to an ambulatory care center. This proposed model is of the same macro process type as that proposed by Urban7 for managing a family planning system. In both cases, the approach has been that the model should stay as simple as possible until additional data are available to support refinements. Stimson and Stimson6 review numerous published approaches to the daily scheduling of patient appointments, but none of these papers has ad

A project was undertaken to determine the need for community services of persons with arthritis. It was first necessary to ascertain patients' viewpoints about what is most needed. A structured interview was developed and submitted to 189 primarily urban patients. Five sources were drawn upon to determine items for the interview: (1) selected arthritis literature; (2) patient interviews; (3) health professionals' viewpoints; (4) functional assessments; and (5) inquiries made to Arthritis Foundation staff. Sixty-six questions based on 25 content areas were then developed. Functional states and demographic and medical information were also solicited. A factor analysis indicated three groups of needs: (1) general information; (2) personal care and medical services; and (3) assistive devices and equipment. Needs were matched with a comprehensive list of countywide resources derived from a computer data bank; a service directory was prepared and printed based on the compatibility between needs and community resources, and distributed to health and social service professionals, selected patients, and community leaders. The process used to select items for the interview was time consuming, but added face validity to the content areas selected. The outcome was vital to determining community resources capable of responding to need for service.

A computerized ward information-management system was developed and implemented at the Johns Hopkins Hospital on one medical floor. To determine its effectiveness, a rigorous evaluation effort was conducted. The evaluation was designed to monitor the effects of the system on patient care by hospital nursing personnel. Likewise, the costs and cost benefits of the incremental expenditures of the system were measured. The results of this evaluation effort showed that the system caused significant changes in personnel work patterns, in that direct patient-care activities increased materially. Errors in carrying out physicians' orders were significantly decreased. Further the evaluation efforts indicated that the costs of the system should be effectively offset by improved staff effectiveness. The system seems to have the potential to help identify those personnel cost savings available if reduced hospital expenses becomes the objective. If the decision is to increase the efficacy of treatment given in the hospital, the ward information-management system seems to provide the ability to improve care on a constant basis without personnel increases.